Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …

Different distribution shifts require different algorithmic and operational interventions.
Methodological research must be grounded by the specific shifts they address. Although …

In differential privacy (DP), a challenging problem is to generate synthetic datasets that
efficiently capture the useful information in the private data. The synthetic dataset enables …

Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger datasets of higher …

The prediction of traffic accident severity is essential for traffic safety management and
control. To achieve high prediction accuracy and model interpretability, we propose a hybrid …

Traffic accident has become a significant health and development threat with rapid
urbanizations. An accurate urban accident forecasting enables higher-quality police force …

The present paper introduces the time between the start of a snowfall and the occurrence of
a motor vehicle accident as a novel measure for evaluating motor vehicle safety during …

Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in
children and young adults. Accident risk maps are useful tools to monitor and mitigate …

Based on millions of traffic accident data in the United States, we build an accident duration
prediction model based on heterogeneous ensemble learning to study the problem of …